Severity prediction markers in dengue: a prospective cohort study using machine learning approach.

IF 2 4区医学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biomarkers Pub Date : 2024-12-01 Epub Date: 2024-11-28 DOI:10.1080/1354750X.2024.2430997

Aashika Raagavi Jean Pierre, Siva Ranganathan Green, Lokeshmaran Anandaraj, Manikandan Sivaprakasam, Anand Kasirajan, Panneer Devaraju, Srilekha Anumulapuri, Srinivasa Rao Mutheneni, Agieshkumar Balakrishna Pillai

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引用次数: 0

Abstract

Background: Dengue virus causes illnesses with or without warning indicators for severe complications. There are no clear prognostic signs linked to the disease outcomes.

Methods: Clinical and laboratory parameters among 102 adult including 17 severe dengue (SD), 33 with warning and 52 without warning signs during early and critical phases were analysed by statistical and machine learning (ML) models.

Results: In classical statistics, abnormal ultrasound findings, platelet count and low lymphocytes were significantly linked with SD during the febrile phase, while low creatinine, high sodium and elevated AST/ALT during the critical phase. ML models highlighted AST/ALT and lymphocytes as key markers for distinguishing SD from non-severe dengue, aiding clinical decisions.

Conclusion: Parameters like liver enzymes, platelet counts and USG findings were linked with SD.USG testing at an earlier phase of dengue and a point-of-care system for the quantification of AST/ALT levels may lead to an early prediction of SD.

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登革热严重程度预测标志物：使用机器学习方法进行的前瞻性队列研究。

背景：登革热病毒导致的疾病会出现或不出现严重并发症的预兆。目前还没有明确的预后征兆与疾病结果相关联：方法：通过统计和机器学习（ML）模型对 102 名成人（包括 17 名重症登革热患者（SD）、33 名有预警征兆的患者和 52 名无预警征兆的患者）在早期和危重期的临床和实验室参数进行分析：在经典统计学中，异常超声波检查结果、血小板计数和低淋巴细胞与发热期的 SD 有显著联系，而低肌酐、高钠和 AST/ALT 升高与危重期的 SD 有显著联系。ML 模型强调 AST/ALT 和淋巴细胞是区分 SD 和非严重登革热的关键指标，有助于临床决策：肝酶、血小板计数和 USG 检查结果等参数与 SD 有关联。在登革热的早期阶段进行 USG 检测，并使用定量 AST/ALT 水平的护理点系统，可及早预测 SD。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biomarkers 医学-毒理学

CiteScore

5.00

自引率

3.80%

发文量

140

审稿时长

3 months

期刊介绍： The journal Biomarkers brings together all aspects of the rapidly growing field of biomarker research, encompassing their various uses and applications in one essential source. Biomarkers provides a vital forum for the exchange of ideas and concepts in all areas of biomarker research. High quality papers in four main areas are accepted and manuscripts describing novel biomarkers and their subsequent validation are especially encouraged: • Biomarkers of disease • Biomarkers of exposure • Biomarkers of response • Biomarkers of susceptibility Manuscripts can describe biomarkers measured in humans or other animals in vivo or in vitro. Biomarkers will consider publishing negative data from studies of biomarkers of susceptibility in human populations.